The most straightforward approach to consider relationships in the image is to construct an inherent hierarchy, where local-level features for identifying local parts are obtained by outputs of CNNs on sub-regions, whereas global-level features for overall understanding of an entire image are obtained by aggregating locally obtained CNN features [1]}, [2]}, [3]}. However, this scheme is highly suboptimal, since it does not explicitly consider semantic and spatial relationships among objects with object representations, but it does implicitly model their relationships with CNN features from different sub-regions. Therefore, in this work, we aim to first detect objects in the given input image with conventional object detection networks (e.g., Faster R-CNN [4]} or RoITransformer [5]}), and then calculate the edges between them with their distances of semantic and spatial features. After that, over the constructed graph with objects as nodes and their distances as edges, we leverage the graph neural networks (GNNs) [6]}, [7]} to obtain graphical features of objects, which are then combined with previously obtained visual CNN features for improving final object prediction performances. Note that, recently, there exist few similar works [8]}, [9]} that consider explicit interactions between all objects by constructing either a fully connected object graph [8]} or a spatial-aware sparse graph [9]}, whose object representations are used to improve CNN features for object detection. However, is it necessary to refine CNN features and detection results of every object?
